SkactivemlClassifier#

class skactiveml.base.SkactivemlClassifier(classes=None, missing_label=nan, cost_matrix=None, random_state=None, target_type='auto')[source]#

Bases: ClassifierMixin, BaseEstimator, ABC

Skactiveml Classifier

Base class for scikit-activeml classifiers such that missing labels, user-defined classes, and cost-sensitive classification (i.e., cost matrix) can be handled.

Parameters:

classesarray-like of shape (n_classes,) or a list of such array-likes, default=None

A flat vocabulary describes single-output classification and is applied to every annotator entry for multi-annotator components.
Nested binary vocabularies describe multi-label classification, one class vocabulary per label output. Nested non-binary vocabularies describe recognized multi-output classification semantics.

missing_labelscalar, string, np.nan, or None, default=np.nan

Value to represent a missing label.

cost_matrixarray-like of shape (n_classes, n_classes)

Cost matrix with cost_matrix[i,j] indicating cost of predicting class classes[j] for a sample of class classes[i]. Can be only set, if classes is not None and one-dimensional, which corresponds to single output classification.

random_stateint or RandomState instance or None, default=None

Determines random number for predict method. Pass an int for reproducible results across multiple method calls.

target_type“auto” or “single-output” or “multi-label” or “multi-output”, default=”auto”

Declared target type. Components reject resolved target specifications outside their exact capabilities.

Attributes:

target_spec_skactiveml.utils.TargetSpec: Immutable target specification established by a successful fit. Its class vocabularies use the canonical ordering of classes_.

Methods

`fit`(X, y[, sample_weight])	Fit the model using X as training data and y as class labels.
`predict`(X, **kwargs)	Return class label predictions for the test samples X.
`predict_proba`(X, **kwargs)	Return probability estimates for the test data X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

abstract SkactivemlClassifier.fit(X, y, sample_weight=None)[source]#

Fit the model using X as training data and y as class labels.

Parameters:

Xarray-like of shape (n_samples, …): The samples X whose shape depends on the respective classifier.
yarray-like of shape (n_samples,) or (n_samples, n_outputs) or (n_samples, n_annotators): Labels of the training data set (possibly including unlabeled ones indicated by missing_label). For multioutput problems, a row y[i] must either contain only observed labels or only missing_label values, i.e., no mixing within a row. For multi-annotator classification, a row can contain labeled and unlabeled entries, where y[i, j] indicates the potential class label for sample X[i] from annotator j.
sample_weightarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: It contains the weights of the training samples. For two- dimensional targets, either one weight per sample or one weight per target entry can be provided.

Returns:

self: skactiveml.base.SkactivemlClassifier: The skactiveml.base.SkactivemlClassifier object fitted on the training data.

SkactivemlClassifier.predict(X, **kwargs)[source]#

Return class label predictions for the test samples X.

Parameters:

Xarray-like of shape (n_samples, …): Input samples.

Returns:

ynumpy.ndarray of shape (n_samples,): Predicted class labels of the test samples X.

SkactivemlClassifier.predict_proba(X, **kwargs)[source]#

Return probability estimates for the test data X.

Parameters:

Xarray-like of shape (n_samples, …): Test samples.

Returns:

Pnumpy.ndarray of shape (n_samples, classes): The class probabilities of the test samples. Classes are ordered according to self.classes_.

SkactivemlClassifier.score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

Parameters:

Xarray-like of shape (n_samples, …): Test samples.
yarray-like of shape (n_samples,): True class labels of the test samples X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights of the test sample X.

Returns:

scorefloat: Mean accuracy of self.predict(X) regarding y.

SkactivemlClassifier.get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

SkactivemlClassifier.get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

SkactivemlClassifier.set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SkactivemlClassifier#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

SkactivemlClassifier.set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

SkactivemlClassifier.set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SkactivemlClassifier#

Configure whether metadata should be requested to be passed to the score method.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns: